{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from warnings import simplefilter\n",
    "simplefilter(action='ignore',category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import arma_order_select_ic\n",
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#绝大多数情况，会从.txt/.xlsx/.csv文件中读取，在此为简化过程，以列表形式给定数据\n",
    "dta=[3345.0, 1905.0, 3860.0, 4492.0, 5726.0, 4992.0, 3443.0, 4053.0, 5105.0, 4236.0, 3673.0, 1932.0, 2685.0, 953.0, 4285.0, 4421.0, 4301.0, 4089.0]\n",
    "# dta=[3345.0, 2905.0, 3860.0, 4492.0, 5726.0, 4992.0, 3443.0, 4053.0, 5105.0, 4236.0, 3673.0, 2932.0, 2685.0, 2953.0, 4285.0, 4421.0, 4301.0, 4089.0]\n",
    "# dta=[363.0, 246.0, 233.0, 226.0, 600.0, 900.0, 5888.682999999999, 6683.379000000003, 7448.271000000001, 6303.769999999998, 4758.706000000001, 4897.457999999998, 4133.019000000001, 1620.1229999999998, 4640.634000000003, 3675.825, 4107.325999999999, 2162.243]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fc26ac8b08>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#转换数据类型，并画出初始序列的变化趋势\n",
    "# dta=pd.Series(dta)\n",
    "dta=pd.Series(np.log(dta))\n",
    "plt.plot(dta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出自相关图\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "ax1=fig.add_subplot(211)\n",
    "fig=plot_acf(dta,lags=10,ax=ax1) #lags代表滞后的阶数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test Statistic Value</th>\n",
       "      <td>-3.04721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0307149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lags Used</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Observations Used</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical Value(1%)</th>\n",
       "      <td>-3.88927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical Value(5%)</th>\n",
       "      <td>-3.05436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical Value(10%)</th>\n",
       "      <td>-2.66698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 value\n",
       "Test Statistic Value          -3.04721\n",
       "p-value                      0.0307149\n",
       "Lags Used                            0\n",
       "Number of Observations Used         17\n",
       "Critical Value(1%)            -3.88927\n",
       "Critical Value(5%)            -3.05436\n",
       "Critical Value(10%)           -2.66698"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#平稳性检测\n",
    "temp=np.array(dta)\n",
    "# temp=np.diff(temp)\n",
    "t=sm.tsa.stattools.adfuller(temp)#ADF检验\n",
    "output=pd.DataFrame(index=['Test Statistic Value', \"p-value\", \"Lags Used\", \"Number of Observations Used\",\"Critical Value(1%)\",\"Critical Value(5%)\",\"Critical Value(10%)\"],columns=['value'])\n",
    "output['value']['Test Statistic Value'] = t[0]\n",
    "output['value']['p-value'] = t[1]\n",
    "output['value']['Lags Used'] = t[2]\n",
    "output['value']['Number of Observations Used'] = t[3]\n",
    "output['value']['Critical Value(1%)'] = t[4]['1%']\n",
    "output['value']['Critical Value(5%)'] = t[4]['5%']\n",
    "output['value']['Critical Value(10%)'] = t[4]['10%']\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#经过ADF检验，我们发现p值并未小于0.05，且T检验值未小于10%\n",
    "#综上所述，已知的时间序列数据为非平稳序列\n",
    "#下面采用ARIMA序列对数据进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fc28ea2e48>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#进行差分\n",
    "ddta=dta.diff(1).dropna()\n",
    "plt.plot(ddta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1406: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return rho, np.sqrt(sigmasq)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(12,8))\n",
    "ax1=fig.add_subplot(211)\n",
    "fig=plot_acf(ddta,lags=10,ax=ax1) #lags代表滞后的阶数\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "ax1=fig.add_subplot(212)\n",
    "fig=plot_pacf(ddta,lags=10,ax=ax1) #lags代表滞后的阶数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test Statistic Value</th>\n",
       "      <td>-6.33818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>2.79692e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lags Used</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Observations Used</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical Value(1%)</th>\n",
       "      <td>-3.92402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical Value(5%)</th>\n",
       "      <td>-3.0685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical Value(10%)</th>\n",
       "      <td>-2.67389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   value\n",
       "Test Statistic Value            -6.33818\n",
       "p-value                      2.79692e-08\n",
       "Lags Used                              0\n",
       "Number of Observations Used           16\n",
       "Critical Value(1%)              -3.92402\n",
       "Critical Value(5%)               -3.0685\n",
       "Critical Value(10%)             -2.67389"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#平稳性检测\n",
    "temp=np.array(ddta)\n",
    "# temp=np.diff(temp)\n",
    "t=sm.tsa.stattools.adfuller(temp)#ADF检验\n",
    "output=pd.DataFrame(index=['Test Statistic Value', \"p-value\", \"Lags Used\", \"Number of Observations Used\",\"Critical Value(1%)\",\"Critical Value(5%)\",\"Critical Value(10%)\"],columns=['value'])\n",
    "output['value']['Test Statistic Value'] = t[0]\n",
    "output['value']['p-value'] = t[1]\n",
    "output['value']['Lags Used'] = t[2]\n",
    "output['value']['Number of Observations Used'] = t[3]\n",
    "output['value']['Critical Value(1%)'] = t[4]['1%']\n",
    "output['value']['Critical Value(5%)'] = t[4]['5%']\n",
    "output['value']['Critical Value(10%)'] = t[4]['10%']\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "差分序列的白噪声检验结果： (array([4.03499639]), array([0.04456577]))\n"
     ]
    }
   ],
   "source": [
    "#对一阶差分后的序列做白噪声检验\n",
    "#p值为第二项\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "print(u'差分序列的白噪声检验结果：',acorr_ljungbox(ddta, lags= 1)) #返回统计量和 p 值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:716: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  invmacoefs = -np.log((1-macoefs)/(1+macoefs))\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:689: RuntimeWarning: invalid value encountered in true_divide\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:690: RuntimeWarning: invalid value encountered in true_divide\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'aic':            0          1          2          3          4\n",
       " 0  31.592377  29.099560  30.147807  30.137618  32.100080\n",
       " 1  29.718828  31.705149  31.678718  32.119371  34.133525\n",
       " 2  31.715280  31.444150  32.980641  31.750485  32.261430\n",
       " 3  33.058446  31.832076        NaN  32.546473  34.962213\n",
       " 4  32.544913  32.869743  34.636916  36.566377  36.546828,\n",
       " 'bic':            0          1          2          3          4\n",
       " 0  33.258804  31.599200  33.480660  34.303685  37.099360\n",
       " 1  32.218468  35.038003  35.844784  37.118651  39.966018\n",
       " 2  35.048134  35.610217  37.979921  37.582978  38.927137\n",
       " 3  37.224513  36.831357        NaN  39.212179  42.461133\n",
       " 4  37.544193  38.702236  41.302623  44.065297  44.878962,\n",
       " 'hqic':            0          1          2          3          4\n",
       " 0  31.758023  29.348029  30.479099  30.551733  32.597019\n",
       " 1  29.967297  32.036442  32.092833  32.616310  34.713286\n",
       " 2  32.046573  31.858265  33.477579  32.330246  32.924015\n",
       " 3  33.472562  32.329015        NaN  33.209057  35.707621\n",
       " 4  33.041852  33.449504  35.299501  37.311784  37.375059,\n",
       " 'aic_min_order': (0, 1),\n",
       " 'bic_min_order': (0, 1),\n",
       " 'hqic_min_order': (0, 1)}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order=sm.tsa.stattools.arma_order_select_ic(ddta,max_ar=4,\n",
    "                             max_ma=4,ic=['aic','bic','hqic'])\n",
    "order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>ARIMA</td>              <td>BIC:</td>         <td>38.0814</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>        <td>D.y</td>          <td>Log-Likelihood:</td>   <td>-14.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2020-10-22 19:10</td>        <td>Scale:</td>        <td>1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>         <td>16</td>               <td>Method:</td>       <td>css-mle</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>2</td>               <td>Sample:</td>          <td>1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>           <td>14</td>                  <td></td>              <td>7</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>1.0000</td>      <td>S.D. of innovations:</td>  <td>0.561</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>No. Iterations:</td>        <td>33.0000</td>             <td>HQIC:</td>        <td>35.882</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>AIC:</td>              <td>35.7637</td>               <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>Coef.</th>  <th>Std.Err.</th>    <th>t</th>     <th>P>|t|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>0.0026</td>   <td>0.0278</td>  <td>0.0941</td>  <td>0.9250</td> <td>-0.0519</td> <td>0.0571</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.y</th> <td>-1.0000</td>  <td>0.1644</td>  <td>-6.0821</td> <td>0.0000</td> <td>-1.3222</td> <td>-0.6777</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>    <th>Real</th>  <th>Imaginary</th> <th>Modulus</th> <th>Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>1.0000</td>  <td>0.0000</td>   <td>1.0000</td>   <td>0.0000</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                         Results: ARIMA\n",
       "=================================================================\n",
       "Model:              ARIMA            BIC:                 38.0814\n",
       "Dependent Variable: D.y              Log-Likelihood:      -14.882\n",
       "Date:               2020-10-22 19:10 Scale:               1.0000 \n",
       "No. Observations:   16               Method:              css-mle\n",
       "Df Model:           2                Sample:              1      \n",
       "Df Residuals:       14                                    7      \n",
       "Converged:          1.0000           S.D. of innovations: 0.561  \n",
       "No. Iterations:     33.0000          HQIC:                35.882 \n",
       "AIC:                35.7637                                      \n",
       "------------------------------------------------------------------\n",
       "               Coef.   Std.Err.     t     P>|t|    [0.025   0.975]\n",
       "------------------------------------------------------------------\n",
       "const          0.0026    0.0278   0.0941  0.9250  -0.0519   0.0571\n",
       "ma.L1.D.y     -1.0000    0.1644  -6.0821  0.0000  -1.3222  -0.6777\n",
       "-------------------------------------------------------------------------\n",
       "                Real          Imaginary         Modulus         Frequency\n",
       "-------------------------------------------------------------------------\n",
       "MA.1           1.0000            0.0000          1.0000            0.0000\n",
       "=================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order=(0,1,1)\n",
    "train=ddta[:-3]\n",
    "test=ddta[-3:]\n",
    "tempModel=ARIMA(ddta,order).fit()\n",
    "tempModel.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0.04054858, 0.04316348, 0.04577838, 0.04839328, 0.05100819,\n",
      "       0.05362309, 0.05623799, 0.05885289, 0.0614678 , 0.0640827 ]), array([0.56139086, 0.56139086, 0.56139086, 0.56139086, 0.56139086,\n",
      "       0.56139086, 0.56139086, 0.56139086, 0.56139086, 0.56139086]), array([[-1.05975728,  1.14085444],\n",
      "       [-1.05714238,  1.14346934],\n",
      "       [-1.05452748,  1.14608424],\n",
      "       [-1.05191258,  1.14869915],\n",
      "       [-1.04929768,  1.15131405],\n",
      "       [-1.04668278,  1.15392895],\n",
      "       [-1.04406787,  1.15654386],\n",
      "       [-1.04145297,  1.15915876],\n",
      "       [-1.03883807,  1.16177367],\n",
      "       [-1.03622317,  1.16438857]]))\n",
      "15    0.031245\n",
      "16   -0.027518\n",
      "17   -0.050547\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(tempModel.forecast(10))\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:583: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc2b1f1e48>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicts = tempModel.predict(16, 18, dynamic=True)\n",
    "print (len(predicts))\n",
    "comp = pd.DataFrame()\n",
    "comp['original'] = test\n",
    "comp['predict'] = predicts\n",
    "comp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1620: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return np.dot(wresid, wresid) / self.df_resid\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0     1     2     3     4     5\n",
      "0   None  None  None  None  None  None\n",
      "1   None  None  None  None  None  None\n",
      "2   None  None  None  None  None  None\n",
      "3   None  None  None  None  None  None\n",
      "4   None  None  None  None  None  None\n",
      "5   None  None  None  None  None  None\n",
      "6   None  None  None  None  None  None\n",
      "7   None  None  None  None  None  None\n",
      "8   None  None  None  None  None  None\n",
      "9   None  None  None  None  None  None\n",
      "10  None  None  None  None  None  None\n",
      "11  None  None  None  None  None  None\n",
      "12  None  None  None  None  None  None\n",
      "13  None  None  None  None  None  None\n",
      "14  None  None  None  None  None  None\n",
      "15  None  None  None  None  None  None\n",
      "16  None  None  None  None  None  None\n",
      "17  None  None  None  None  None  None\n",
      "18  None  None  None  None  None  None\n",
      "19  None  None  None  None  None  None\n",
      "20  None  None  None  None  None  None\n",
      "21  None  None  None  None  None  None\n",
      "22  None  None  None  None  None  None\n",
      "23  None  None  None  None  None  None\n",
      "24  None  None  None  None  None  None\n",
      "25  None  None  None  None  None  None\n",
      "26  None  None  None  None  None  None\n",
      "27  None  None  None  None  None  None\n",
      "28  None  None  None  None  None  None\n",
      "29  None  None  None  None  None  None\n",
      "30  None  None  None  None  None  None\n",
      "31  None  None  None  None  None  None\n",
      "32  None  None  None  None  None  None\n",
      "33  None  None  None  None  None  None\n",
      "34  None  None  None  None  None  None\n",
      "35  None  None  None  None  None  None\n",
      "*************************************************************\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "G:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:214: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "reduction operation 'argmin' not allowed for this dtype",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-c7344e0d349f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbic_matrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"*************************************************************\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbic_matrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midxmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m   \u001b[1;31m#先使用stack 展平， 然后使用 idxmin 找出最小值的位置\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'BIC 最小的p值 和 q 值：%s,%s'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m#  BIC 最小的p值 和 q 值：0,1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mG:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36midxmin\u001b[1;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2037\u001b[0m         \"\"\"\n\u001b[0;32m   2038\u001b[0m         \u001b[0mskipna\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalidate_argmin_with_skipna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2039\u001b[1;33m         \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnanops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnanargmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2040\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2041\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mG:\\MyDownLoads\\Anaconda3\\lib\\site-packages\\pandas\\core\\nanops.py\u001b[0m in \u001b[0;36m_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m                 \u001b[0mf_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nan\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m                 raise TypeError(\n\u001b[1;32m---> 65\u001b[1;33m                     \u001b[1;34mf\"reduction operation '{f_name}' not allowed for this dtype\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m                 )\n\u001b[0;32m     67\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: reduction operation 'argmin' not allowed for this dtype"
     ]
    }
   ],
   "source": [
    "# pmax = int(len(ddta) / 10)    #一般阶数不超过 length /10\n",
    "# qmax = int(len(ddta) / 10)\n",
    "pmax = 6    #一般阶数不超过 length /10\n",
    "qmax = 6\n",
    "bic_matrix = []\n",
    "for p in range(1,(pmax +1)):\n",
    "    temp= []\n",
    "    for q in range(1,(qmax+1)):\n",
    "        try:\n",
    "            temp.append(ARIMA(ddta, (p, 1, q)).fit().bic)\n",
    "        except:\n",
    "            temp.append(None)\n",
    "        bic_matrix.append(temp)\n",
    "\n",
    "bic_matrix = pd.DataFrame(bic_matrix)   #将其转换成Dataframe 数据结构\n",
    "print(bic_matrix)\n",
    "print(\"*************************************************************\")\n",
    "p,q = bic_matrix.stack().idxmin()   #先使用stack 展平， 然后使用 idxmin 找出最小值的位置\n",
    "print(u'BIC 最小的p值 和 q 值：%s,%s' %(p,q))  #  BIC 最小的p值 和 q 值：0,1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
